A computer-implemented method for providing a trained function for performing a workpiece quality control includes receiving a plurality of training machining datasets, wherein different training high-frequency machining datasets are representative for the quality of different workpieces, transforming the plurality of training machining datasets into the time-frequency domain to generate a plurality of training time-frequency domain datasets, and training a function based on the plurality of training time-frequency domain datasets, wherein the function is based on an autoencoder. The autoencoder has input layers, output layers and a hidden layer. The plurality of training time-frequency domain datasets are provided to the input layers and the output layers during training, and a trained autoencoder function is outputted.
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2. The computer-implemented method of claim 1, wherein the training machining datasets are based on time-series CNC machining data.
3. The computer-implemented method of claim 1, wherein the statistical distribution is a normal distribution, in particular a normal distribution with mean value equal to zero and variance equal to one, or uniform distribution.
4. The computer-implemented method of claim 1, wherein the transforming is performed by means of the wavelet transformation.
5. The computer-implemented method of claim 1, wherein the autoencoder is a recurrent neural network, in particular a long short-term memory network.
6. A computer program product comprising a computer program stored on a non-transitory computer-readable medium and comprising instructions which, when the program is loaded into a memory of a computing platform and executed by a processor of the computing platform, causes the computing platform to carry out the method of claim 1.
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February 17, 2021
September 3, 2024
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